Data Solution Architect

Falcon Smart IT (FalconSmartIT); San Mateo (CA); On-site; USD 180,000 - 240,000; Full time

Job summary

Falcon Smart IT is seeking a seasoned Data Engineering Lead to drive Databricks-based data platforms on AWS with a strong emphasis on MDM and enterprise data integration. You will guide governance, data quality, and scalable analytics solutions across business functions.

The role requires deep cloud-based data engineering experience, middleware integrations, and collaboration with business, architecture, analytics, and engineering teams to modernize data platforms for trusted insights.

Qualifications

• Proven track record delivering enterprise-scale data platforms.
• Strong experience with Databricks on AWS and Lakehouse architecture.
• Expertise in MDM, data governance, lineage, and data quality.
• Experience leading distributed data engineering teams and managing stakeholders.

Responsibilities

• Lead design and delivery of Databricks-based data platforms on AWS.
• Architect scalable Lakehouse solutions for analytics workloads.
• Develop complex ETL/ELT pipelines using Databricks, Spark, and cloud services.
• Drive MDM strategy, implementation, and enterprise integration.
• Define data integration patterns using APIs, middleware, and event-driven architectures.
• Mentor engineers and guide architecture reviews and governance.

Skills

Databricks on AWS

Apache Spark / PySpark

Lakehouse architecture

MDM / data governance

APIs / middleware integrations

SQL & Python development

Leadership of distributed teams

Agile / DevOps practices

Tools

Databricks

MuleSoft / Boomi / Kafka

Kafka / Kinesis

Informatica IDMC / Talend / Azure Data Factory

Snowflake / Microsoft Fabric

Job description

• 10+ years of experience in Data Engineering, Data Integration, or Data Platform delivery.
• Strong hands-on experience with Databricks on AWS.
• Expertise in Apache Spark, PySpark, Delta Lake, and Lakehouse architecture.
• Experience designing and implementing enterprise-scale data pipelines.
• Strong understanding of AWS services such as S3, Glue, Lambda, Redshift, IAM, and CloudWatch.
• Hands-on experience with MDM implementations and integrations.
• Experience with data quality, data governance, lineage, and master data management processes.
• Strong experience integrating enterprise systems using middleware platforms such as MuleSoft, Boomi, Kafka, or API-based integrations.
• Experience working with structured, semi-structured, and unstructured datasets.
• Strong SQL and Python development skills.
• Experience with Agile delivery methodologies and DevOps practices.
• Experience leading distributed teams and managing stakeholder communications.

Secondary Skills (Good to Have)

• Experience with Snowflake or Microsoft Fabric.
• Exposure to AI/ML enablement using Databricks ML or AWS SageMaker.
• Experience with Unity Catalog and data governance frameworks.
• Knowledge of Data Mesh and Data Product concepts.
• Experience with real-time streaming using Kafka or Kinesis.
• Experience with Informatica IDMC, Talend, or Azure Data Factory.
• Knowledge of Healthcare, life sciences, retail, manufacturing, or financial services domains.
• Exposure to GenAI and enterprise AI adoption initiatives.

Role Summary

• Client is seeking a seasoned Data Engineering Lead with strong expertise in Databricks on AWS, MDM, and enterprise data integration.
• The candidate will lead the design and delivery of modern data platforms that enable trusted, governed, and scalable data consumption across business functions.
• This role requires deep experience in cloud-based data engineering, middleware integrations, data governance, and enterprise-scale analytics solutions.
• The ideal candidate will work closely with business, architecture, analytics, and engineering teams to drive data modernization initiatives.

Why This Role Matters

• This role will be instrumental in enabling the client's enterprise data modernization journey.
• The individual will help establish a scalable and governed data foundation that supports advanced analytics, AI/ML initiatives, and business decision-making.
• Success in this role will directly improve data quality, consistency, and accessibility across critical business domains.

What You'll Do

• Lead the design and implementation of Databricks-based data platforms on AWS.
• Architect scalable Lakehouse solutions supporting enterprise analytics workloads.
• Design and develop complex ETL/ELT pipelines using Databricks, Spark, and cloud-native services.
• Drive MDM strategy, implementation, and integration across business applications and data platforms.
• Define data integration patterns using APIs, middleware, event-driven architectures, and messaging frameworks.
• Collaborate with business stakeholders to understand data requirements and translate them into technical solutions.
• Establish data governance, metadata management, and data quality frameworks.
• Optimize data processing performance, scalability, and operational monitoring.
• Define CI/CD processes and deployment standards for data engineering assets.
• Mentor engineering teams and provide technical leadership throughout the project lifecycle.
• Support architecture reviews, solution design discussions, and technical decision-making.
• Ensure compliance with organizational standards, security requirements, and best practices.

Soft Skills & Other Skills

Communication Skills

• Ability to communicate complex technical concepts to business and executive stakeholders.
• Effective verbal, written, and presentation skills.
• Facilitate architecture reviews, workshops, and stakeholder discussions.

Stakeholder Management

• Build strong relationships with business, IT, and external partners.
• Manage competing priorities and drive consensus among stakeholders.
• Demonstrate customer-centric and consultative engagement skills.

Leadership Skills

• Lead cross-functional and geographically distributed teams.
• Mentor and guide engineers and junior architects.
• Influence technical decisions through collaboration.

Problem Solving & Analytical Thinking

• Identify root causes of complex data and integration challenges.
• Evaluate multiple solution options and recommend optimal approaches.
• Strong troubleshooting and performance optimization capabilities.

What Success Looks Like (6-12 Months)

• Successfully deliver enterprise-scale Databricks capabilities.
• Establish a governed and scalable Lakehouse architecture for analytics and reporting.
• Improve master data consistency and data quality across key business domains.
• Implement standardized integration frameworks and reusable patterns.
• Enable reliable and efficient data movement across multiple enterprise systems.
• Achieve stakeholder confidence through consistent delivery and technical leadership.
• Establish engineering best practices, automation, and governance processes.
• Become a trusted advisor for data platform and integration strategy.

Why Join Us

• Client's platform: Microsoft Partner of the Year (2025), Great Place to Work certified, 19+ years of enterprise delivery excellence.
• Lead large-scale cloud and data transformation engagements for Fortune 500 customers.
• Opportunity to architect modern Databricks Lakehouse solutions from the ground up.
• Work on cutting-edge Data & Analytics, MDM, and AI-enabled platforms.
• Collaborate with highly skilled cloud, data, and architecture teams.
• Exposure to enterprise-scale AWS and multi-cloud transformation programs.
• Accelerate your growth into Solution Architect, Principal Architect, or Data Practice leadership roles.
• Be part of Client's innovation-driven culture focused on modern data and AI solutions.